Integration of GIS and Remote Sensing with RUSLE Model for Estimation of Soil Erosion
Amlan Ghosh,
Sayandeep Rakshit,
Suvarna Tikle,
Sandipan Das,
Uday Chatterjee,
Chaitanya B. Pande,
Abed Alataway,
Ahmed A. Al-Othman,
Ahmed Z. Dewidar,
Mohamed A. Mattar
Affiliations
Amlan Ghosh
Symbiosis Institute of Geo-Informatics, Symbiosis International (Deemed University), Pune 411016, Maharashtra, India
Sayandeep Rakshit
Symbiosis Institute of Geo-Informatics, Symbiosis International (Deemed University), Pune 411016, Maharashtra, India
Suvarna Tikle
Environmental Modeling Division, Max Planck Institute for Meteorology, Bundesstrasse 53, 20146 Hamburg, Germany
Sandipan Das
Symbiosis Institute of Geo-Informatics, Symbiosis International (Deemed University), Pune 411016, Maharashtra, India
Uday Chatterjee
Department of Geography, Bhatter College, Dantan (Vidyasagar University), Paschim Medinipur 721426, West Bengal, India
Chaitanya B. Pande
Indian Institute of Tropical Meteorology, Pune 411008, Maharashtra, India
Abed Alataway
Prince Sultan Bin Abdulaziz International Prize for Water Chair, Prince Sultan Institute for Environmental, Water and Desert Research, King Saud University, Riyadh 11451, Saudi Arabia
Ahmed A. Al-Othman
Department of Agricultural Engineering, College of Food and Agriculture Sciences, King Saud University, Riyadh 11451, Saudi Arabia
Ahmed Z. Dewidar
Prince Sultan Bin Abdulaziz International Prize for Water Chair, Prince Sultan Institute for Environmental, Water and Desert Research, King Saud University, Riyadh 11451, Saudi Arabia
Mohamed A. Mattar
Prince Sultan Bin Abdulaziz International Prize for Water Chair, Prince Sultan Institute for Environmental, Water and Desert Research, King Saud University, Riyadh 11451, Saudi Arabia
Globally, soil erosion is a significant problem contributing to nutrient loss, water quality degradation, and sand accumulation in water bodies. Currently, various climate factors are affecting the natural resources entire worldwide. Agricultural intensification, soil degradation, and some other human impacts all contribute to soil erosion, which is a significant issue. Management and conservation efforts in a watershed can benefit from a soil erosion study. Modeling can establish a scientific and accurate method to calculate sediment output and soil erosion below a variety of circumstances. The measured soil loss tolerance was compared to the risk of soil erosion (T value).In this study, GIS and remote sensing techniques have been integrated with the Revised Universal Soil Loss Equation (RUSLE) model to estimate soil loss in the Mayurakshi river basin of eastern India. To determine soil erosion-prone areas, rainfall, land use, and land cover maps, as well as a digital elevation model (DEM), were used as input. The annual soil loss in the basin area is estimated to be 4,629,714.8 tons. Accordingly, the study basin was categorized into five soil loss severity classes: very low (40.92%), low (49%), moderate (6.5%), high (2.4%) and very high (1.18%) risk classes. Soil erosion rates ranged from very slight to slight throughout the majority of the region. The section of the basin’s lower plain has been discovered to be least affected by soil loss. The results of study area can be helpful to conservation of soil management practices and watershed development program in the basin area.
